




'''simple docstring'''


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


UpperCAmelCase_					=   r"\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `\" / \"`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `\" // \"`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `\"train\"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `\"compressed\"`)\n            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n            `\"compressed\"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n"





@add_start_docstrings(__magic_name__			)
class  __lowercase    (	__magic_name__			):
   _a    						=   """rag"""
   _a    						=   True







   def __init__(						self   ,				UpperCamelCase=None   ,				UpperCamelCase=True   ,				UpperCamelCase=None   ,				UpperCamelCase=None   ,				UpperCamelCase=None   ,				UpperCamelCase=None   ,				UpperCamelCase=None   ,				UpperCamelCase=" / "   ,				UpperCamelCase=" // "   ,				UpperCamelCase=5   ,				UpperCamelCase=300   ,				UpperCamelCase=768   ,				UpperCamelCase=8   ,				UpperCamelCase="wiki_dpr"   ,				UpperCamelCase="train"   ,				UpperCamelCase="compressed"   ,				UpperCamelCase=None   ,				UpperCamelCase=None   ,				UpperCamelCase=False   ,				UpperCamelCase=False   ,				UpperCamelCase=0.0   ,				UpperCamelCase=True   ,				UpperCamelCase=False   ,				UpperCamelCase=False   ,				UpperCamelCase=False   ,				UpperCamelCase=True   ,				UpperCamelCase=None   ,				**UpperCamelCase   ,				)					->     int:
     super().__init__(
         bos_token_id=UpperCamelCase   ,				pad_token_id=UpperCamelCase   ,				eos_token_id=UpperCamelCase   ,				decoder_start_token_id=UpperCamelCase   ,				forced_eos_token_id=UpperCamelCase   ,				is_encoder_decoder=UpperCamelCase   ,				prefix=UpperCamelCase   ,				vocab_size=UpperCamelCase   ,				**UpperCamelCase   ,				)
     assert (
         "question_encoder" in kwargs and "generator" in kwargs
     ), "Config has to be initialized with question_encoder and generator config"
     __a								=       kwargs.pop('question_encoder'   )
     __a								=       question_encoder_config.pop('model_type'   )
     __a								=       kwargs.pop('generator'   )
     __a								=       decoder_config.pop('model_type'   )

     from ..auto.configuration_auto import AutoConfig

     __a								=       AutoConfig.for_model(UpperCamelCase   ,				**UpperCamelCase   )
     __a								=       AutoConfig.for_model(UpperCamelCase   ,				**UpperCamelCase   )

     __a								=       reduce_loss
     __a								=       label_smoothing
     __a								=       exclude_bos_score
     __a								=       do_marginalize

     __a								=       title_sep
     __a								=       doc_sep
     __a								=       n_docs
     __a								=       max_combined_length

     __a								=       dataset
     __a								=       dataset_split
     __a								=       index_name

     __a								=       retrieval_vector_size
     __a								=       retrieval_batch_size
     __a								=       passages_path
     __a								=       index_path
     __a								=       use_dummy_dataset

     __a								=       output_retrieved

     __a								=       do_deduplication

     __a								=       use_cache

     if self.forced_eos_token_id is None:
       __a								=       getattr(self.generator   ,				'forced_eos_token_id'   ,				UpperCamelCase   )







   @classmethod
   def 		UpperCamelCase__      (						cls   ,				UpperCamelCase   ,				UpperCamelCase   ,				**UpperCamelCase   )					->     PretrainedConfig:
     return cls(question_encoder=question_encoder_config.to_dict()   ,				generator=generator_config.to_dict()   ,				**UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       copy.deepcopy(self.__dict__   )
     __a								=       self.question_encoder.to_dict()
     __a								=       self.generator.to_dict()
     __a								=       self.__class__.model_type
     return output









'''simple docstring'''


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate





@dataclass(frozen=__magic_name__			)
class  __lowercase    (	__magic_name__			):
   _a    						=   field(default="""audio-classification"""							,				metadata={"""include_in_asdict_even_if_is_default""": True}			)
   _a    						=   Features({"""audio""": Audio()}			)
   _a    						=   Features({"""labels""": ClassLabel}			)
   _a    						=   "audio"
   _a    						=   "labels"







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Dict:
     if self.label_column not in features:
       raise ValueError(f"Column {self.label_column} is not present in features."   )
     if not isinstance(features[self.label_column]   ,				UpperCamelCase   ):
       raise ValueError(f"Column {self.label_column} is not a ClassLabel."   )
     __a								=       copy.deepcopy(self   )
     __a								=       self.label_schema.copy()
     __a								=       features[self.label_column]
     __a								=       label_schema
     return task_template







   @property
   def 		UpperCamelCase__      (						self   )					->     Dict[str, str]:
     return {
         self.audio_column: "audio",
         self.label_column: "labels",
     }



